Phosphorus adsorption characteristics in forested and managed podzolic soils
Bibliographic record
Abstract
Abstract Despite the agricultural expansion into Canada's boreal ecoregion, little is known about the phosphorus (P) adsorption capacity in natural and managed Podzols' soil profiles. This information is critical for informing management decisions for P use efficiency and mitigating related environmental risks. Thus, this study aimed to evaluate P adsorption characteristics of podzolic horizons in natural and managed soil using nonlinear Langmuir and Freundlich adsorption models. A batch adsorption experiment was conducted using soils collected from distinct horizons of forested and managed fields in eastern and central Newfoundland, Canada. Nonlinear Langmuir and Freundlich fitted models had r values >.99 regardless of horizons, locations, and management history. The organic LFH, a surface horizon typical for forested Podzols, and a long‐term managed Ap horizon had the highest P retention capacities when compared to either newly converted soils or soils used as tree nursery following conversion from natural forest. A significant linear correlation and multiple regression models ( p < .05) were established between P adsorption parameters and selected soil properties. Results suggest that following conversion from forest to agricultural use, long‐term management that includes tillage and lime and fertilizer application creates an Ap horizon with strong adsorption capacity, which could still fix P and serve as a source of P. The newly converted soils and the deeper soil horizons, for both natural and converted lands, do act mainly as P sinks; hence, crops may need larger quantities of P fertilizer increasing fertilizer expenses and also increasing future legacy P.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".